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  <doc>
    <id>4566</id>
    <completedYear/>
    <publishedYear>2014</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>report</type>
    <publisherName>Agora Energiewende</publisherName>
    <publisherPlace>Berlin</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Stromspeicher in der Energiewende - Untersuchung zum Bedarf an neuen Stromspeichern in Deutschland für den Erzeugungsausgleich, Systemdienstleistungen und im Verteilnetz</title>
    <abstract language="deu">Wie groß ist der Speicherbedarf in Deutschland in der weiteren Umsetzung der Energiewende? Welche Rolle spielen Batteriespeicher, Pumpspeicher, Power-to-Gas etc. im Kontext anderer Flexibilitätsoptionen auf den verschiedenen Netzebenen? Wie entwickelt sich der Markt für Batterien und Wasserstoff? In unserer Agora-Speicherstudie haben wir auch erstmalig den Begriff Power-to-X definiert und damit die bis dato entstandenen Begriffe Power-to-Gas, Power-to-Liquids, Power-to-Products, Power-to-Chemicals etc. zusammengefasst.</abstract>
    <subTitle language="deu">Studie</subTitle>
    <identifier type="doi">10.13140/RG.2.2.31804.56964</identifier>
    <enrichment key="opus.import.date">2022-06-30T19:37:23+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Michael Sterner</author>
    <author>Martin Thema</author>
    <author>Fabian Eckert</author>
    <author>Albert Moser</author>
    <author>Andreas Schäfer</author>
    <author>Tim Drees</author>
    <author> Christian Rehtanz</author>
    <author> Ulf Häger</author>
    <author>Jan Kays</author>
    <author>André Seack</author>
    <author> Dirk Uwe Sauer</author>
    <author> Matthias Leuthold</author>
    <author> Philipp Stöcker</author>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16312">Energie und Mobilität</collection>
    <collection role="persons" number="sternerfenes">Sterner, Michael (Prof. Dr.) - FENES / Forschungsgruppe Energiespeicher</collection>
  </doc>
  <doc>
    <id>5894</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>15</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2023-03-09</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Bus Route Classification for Rural Areas using Graph Convolutional Networks</title>
    <abstract language="eng">In this paper, we present a new approach to determine the estimated time of arrival (ETA) for bus routes using (Deep) Graph Convolutional Networks (DGCNs). In addition we use the same DGCN to detect detours within a route. In our application, a classification of routes and their underlying graph structure is performed using Graph Learning. Our model leads to a fast prediction and avoids solving the vehicle routing problem (VRP) through expensive computations. Moreover, we describe how to predict travel time for all routes using the same DGCN Model. This method makes it possible not to use a more computationally intensive approximation algorithm when determining long travel times with many intermediate stops, but to use our network for an early estimate of the quality of a route. Long travel times, in our case result from the use of a call-bus system, which must distribute many passengers among several vehicles and can take them to places without a regular stop. For a case study, the rural town of Roding in Bavaria is used. Our training data for this area results from an approximation algorithm that we implemented to optimize routes, and to generate an archive of routes of varying quality simultaneously.</abstract>
    <parentTitle language="eng">Proceedings of the 22th International Conference on Artificial Intelligence and Soft Computing, Zakopane, Poland</parentTitle>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="ConferenceStatement">22nd International Conference on Artificial Intelligence and Soft Computing (ICAISC 2023), June 18-22, 2023, Zakopane, Poland</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Jan Dünnweber</author>
    <author>Timo Stadler</author>
    <author>Sandra Weikl</author>
    <author>Andreas Schäfer</author>
    <author>Peter Georg</author>
    <author>Simon Wein</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="RCAI">Regensburg Center for Artificial Intelligence - RCAI</collection>
    <collection role="institutes" number="">Labor Parallele und Verteilte Systeme</collection>
  </doc>
</export-example>
